Predictive Analysis of Academic Performance Among Students using A-CNN-BiLSTM Approach
- Title
- Predictive Analysis of Academic Performance Among Students using A-CNN-BiLSTM Approach
- Creator
- Nishant N.; Magadum A.; Pandey V.; Suganthi D.; Rashmi Bh.; Vigneshwaran K.
- Description
- The number of possibilities to analyze educational data using data mining techniques is expanding, with the goal of improving learning outcomes. There is an explosion in data produced by online and virtual education, e-learning platforms, and institutional IT. Using these statistics, teachers could gain valuable insights into their students' learning habits. Academic performance of students and other useful information can be analyzed with the help of educational data mining. Model training consists of three primary steps: data preprocessing, feature selection, and training the model. To eliminate unwanted problems like noise and redundant attributes, data preparation is necessary. By prioritizing which features to calculate, the mRMR algorithm lowers calculation costs. Feature selection plays a crucial role in training A-CNN-BiLSTM models. The suggested approach routinely outperforms BiLSTM and CNN, two state-of-the-art algorithms. With a data accuracy percentage of 96.57%, it's clear that there was a significant improvement. 2024 IEEE.
- Source
- 2024 2nd World Conference on Communication and Computing, WCONF 2024
- Date
- 2024-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Academic Performance; Convolutional Neural Networks (CNN); Minimum Redundancy Maximum Relevance (mRMR)
- Coverage
- Nishant N., Department of CSE, School of Engineering, Babu Banarasi Das University, Lucknow, India; Magadum A., Department of MCA, MIT-ADT University, College of Management, Maharashtra, Pune, India; Pandey V., Amity University, Patna, India; Suganthi D., Department of Computational Intelligence, Saveetha College of Liberal Arts and Sciences, SIMATS, Chennai, India; Rashmi Bh., School of Business and Management, Christ University, Bengaluru, India; Vigneshwaran K., Department of ECE, K.Ramakrishnan College of Engineering, Trichy, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835039532-7
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Nishant N.; Magadum A.; Pandey V.; Suganthi D.; Rashmi Bh.; Vigneshwaran K., “Predictive Analysis of Academic Performance Among Students using A-CNN-BiLSTM Approach,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/19145.